Procesamiento de Lenguage Natural¶
Taller #6: Word2Vec¶
Fecha de entrega: Septiembre 19, 2020. (Antes del inicio de la próxima clase).
Este es un trabajo colectivo realizado por:
Angela Villate
Leonardo Rubio
Libre elección¶
Expectativas:
- Uso de Word2Vec
- Responder:
- ¿Su modelo da buenos resultados? ¿Por qué sà o por qué no?
- ¿Qué problemas encontró al realizar este taller?
Enviar código con explicaciones del por qué escogieron ese camino¶
Primeros pasos de nuestro proyecto¶
Quicimos aprovechar este ejercicio para analizar parte de los datos que hemos recolectado (hasta el momento) en nuestro proyecto sobre "análisis de sentimientos en twiter". Para contextualizar, nuestro proyecto consiste en el análisis de sentimientos positivos y negativos sobre pelÃculas. Para este ejercicio acudimos a dos fuentes: un dataset de IMBD (sobre pelÃculas) y la recolección de twits cuyos hashtags estuviesen relacionados con las pelÃculas seleccionadas.
Los twits que se recopilaron fueron tomados de la fecha de lanzamiento de la pelÃcula.
En el marco de la clase de NLP queremos comprender cuál es el comportamiento o relación vectorial de los términos que cualifican las pelÃculoas en su contexto de uso.
Para lograrlo, hicimos lo siguiente:
- Proceso de recolección de los twits por cada una de las pelÃculas seleccionadas
- Generamos un embedding personalizado con un modelo skip -gram
- Visualizamos las distancias de los vectores
- Comparamos los resultados con la lista de emebding en inglés (para contar con un parámetro de referencia semántico)
- Analizamos las relaciones lingüÃsticas para cada uno de los casos
Como siempre, hacemos el cargue librerias para tener nuestro entorno de trabajo
In [1]:
!pip install tweepy
!pip install gensim
import re
import numpy as np
import pandas as pd
import tweepy
from tweepy import OAuthHandler
import json
Requirement already satisfied: tweepy in c:\users\villa\anaconda3\lib\site-packages (3.9.0) Requirement already satisfied: requests[socks]>=2.11.1 in c:\users\villa\anaconda3\lib\site-packages (from tweepy) (2.23.0) Requirement already satisfied: six>=1.10.0 in c:\users\villa\anaconda3\lib\site-packages (from tweepy) (1.14.0) Requirement already satisfied: requests-oauthlib>=0.7.0 in c:\users\villa\anaconda3\lib\site-packages (from tweepy) (1.3.0) Requirement already satisfied: chardet<4,>=3.0.2 in c:\users\villa\anaconda3\lib\site-packages (from requests[socks]>=2.11.1->tweepy) (3.0.4) Requirement already satisfied: certifi>=2017.4.17 in c:\users\villa\anaconda3\lib\site-packages (from requests[socks]>=2.11.1->tweepy) (2019.11.28) Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in c:\users\villa\anaconda3\lib\site-packages (from requests[socks]>=2.11.1->tweepy) (1.25.8) Requirement already satisfied: idna<3,>=2.5 in c:\users\villa\anaconda3\lib\site-packages (from requests[socks]>=2.11.1->tweepy) (2.8) Requirement already satisfied: PySocks!=1.5.7,>=1.5.6; extra == "socks" in c:\users\villa\anaconda3\lib\site-packages (from requests[socks]>=2.11.1->tweepy) (1.7.1) Requirement already satisfied: oauthlib>=3.0.0 in c:\users\villa\anaconda3\lib\site-packages (from requests-oauthlib>=0.7.0->tweepy) (3.1.0) Requirement already satisfied: gensim in c:\users\villa\anaconda3\lib\site-packages (3.8.3) Requirement already satisfied: numpy>=1.11.3 in c:\users\villa\anaconda3\lib\site-packages (from gensim) (1.18.1) Requirement already satisfied: scipy>=0.18.1 in c:\users\villa\anaconda3\lib\site-packages (from gensim) (1.4.1) Requirement already satisfied: smart-open>=1.8.1 in c:\users\villa\anaconda3\lib\site-packages (from gensim) (2.1.1) Requirement already satisfied: six>=1.5.0 in c:\users\villa\anaconda3\lib\site-packages (from gensim) (1.14.0) Requirement already satisfied: Cython==0.29.14 in c:\users\villa\anaconda3\lib\site-packages (from gensim) (0.29.14) Requirement already satisfied: boto3 in c:\users\villa\anaconda3\lib\site-packages (from smart-open>=1.8.1->gensim) (1.15.1) Requirement already satisfied: requests in c:\users\villa\anaconda3\lib\site-packages (from smart-open>=1.8.1->gensim) (2.23.0) Requirement already satisfied: boto in c:\users\villa\anaconda3\lib\site-packages (from smart-open>=1.8.1->gensim) (2.49.0) Requirement already satisfied: jmespath<1.0.0,>=0.7.1 in c:\users\villa\anaconda3\lib\site-packages (from boto3->smart-open>=1.8.1->gensim) (0.10.0) Requirement already satisfied: botocore<1.19.0,>=1.18.1 in c:\users\villa\anaconda3\lib\site-packages (from boto3->smart-open>=1.8.1->gensim) (1.18.1) Requirement already satisfied: s3transfer<0.4.0,>=0.3.0 in c:\users\villa\anaconda3\lib\site-packages (from boto3->smart-open>=1.8.1->gensim) (0.3.3) Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in c:\users\villa\anaconda3\lib\site-packages (from requests->smart-open>=1.8.1->gensim) (1.25.8) Requirement already satisfied: certifi>=2017.4.17 in c:\users\villa\anaconda3\lib\site-packages (from requests->smart-open>=1.8.1->gensim) (2019.11.28) Requirement already satisfied: chardet<4,>=3.0.2 in c:\users\villa\anaconda3\lib\site-packages (from requests->smart-open>=1.8.1->gensim) (3.0.4) Requirement already satisfied: idna<3,>=2.5 in c:\users\villa\anaconda3\lib\site-packages (from requests->smart-open>=1.8.1->gensim) (2.8) Requirement already satisfied: python-dateutil<3.0.0,>=2.1 in c:\users\villa\anaconda3\lib\site-packages (from botocore<1.19.0,>=1.18.1->boto3->smart-open>=1.8.1->gensim) (2.8.1)
Parámetros para aplicacion twiter
In [171]:
api_key = "xxxxxxxxxxxxxxxxxxxxxxx"
api_secret = "xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"
access_token = "xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"
access_secret = "xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"
auth = OAuthHandler(api_key, api_secret)
auth.set_access_token(access_token, access_secret)
api = tweepy.API(auth)
In [172]:
public_tweets = api.home_timeline()
Clase de conexion con twiter
In [186]:
class MyStreamListener(tweepy.StreamListener):
def __init__(self, api):
self.api = api
self.me = api.me()
self.batch_size = 3
def on_status(self, tweet):
print(f"{tweet.user.name}:{tweet.text}georef-{tweet.created_at}")
def on_error(self, status):
print("Error detected")
En este punto se establecieron los parámetros para las búsquedas de twiter
In [69]:
LOCATIONS = [-124.7771694, 24.520833, -66.947028, 49.384472, # Contiguous US
-164.639405, 58.806859, -144.152365, 71.76871, # Alaska
-160.161542, 18.776344, -154.641396, 22.878623] # Hawaii
track = ["Python"]
languages = ['es']
date_since = "2020-5-21"
Para la limpieza de los datos se decidió dejar: letras, números, @ y # (estos últimos porque tenemos la intuición de que pueden agregar valor semántico al mensaje)
In [174]:
cadena=""
def pre_procesado(texto):
texto = texto.lower()
#texto = re.sub(r"[\W\d_]+", " ", texto)
texto = re.sub(r"[^A-z 0-9 @ #]+", " ", texto)
#texto = " ".join([palabra for palabra in texto.split() if palabra not in stopwords_sp])
#texto = " ".join([spanishStemmer.stem(palabra) for palabra in texto.split() ])
return texto
Se realicó el cargue del archivo excel
In [2]:
datos_excel= pd.read_excel("movies_filter.xlsx")
datos_excel
Out[2]:
| budget | company | country | director | genre | gross | name | rating | day_released | month_released | year_released | released | runtime | score | star | votes | writer | year | hashtag | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 100000000.0 | Red Granite Pictures | USA | Martin Scorsese | Biography | 116900694.0 | The Wolf of Wall Street | R | 25 | 12 | 2013 | 2013-12-25 | 180 | 8.2 | Leonardo DiCaprio | 895552 | Terence Winter | 2013 | #TheWolfofWallStreet |
| 1 | 20000000.0 | New Line Cinema | USA | James Wan | Horror | 137400141.0 | The Conjuring | R | 19 | 7 | 2013 | 2013-07-19 | 112 | 7.5 | Patrick Wilson | 342870 | Chad Hayes | 2013 | #TheConjuring |
| 2 | 46000000.0 | Alcon Entertainment | USA | Denis Villeneuve | Crime | 61002302.0 | Prisoners | R | 20 | 9 | 2013 | 2013-09-20 | 153 | 8.1 | Hugh Jackman | 449489 | Aaron Guzikowski | 2013 | #Prisoners |
| 3 | 150000000.0 | Walt Disney Animation Studios | USA | Chris Buck | Animation | 400738009.0 | Frozen | PG | 27 | 11 | 2013 | 2013-11-27 | 102 | 7.5 | Kristen Bell | 464149 | Jennifer Lee | 2013 | #Frozen |
| 4 | 75000000.0 | Summit Entertainment | USA | Louis Leterrier | Crime | 117723989.0 | Now You See Me | PG-13 | 31 | 5 | 2013 | 2013-05-31 | 115 | 7.3 | Jesse Eisenberg | 505432 | Ed Solomon | 2013 | #NowYouSeeMe |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 718 | 0.0 | Fox Searchlight Pictures | UK | Mandie Fletcher | Comedy | 4750497.0 | Absolutely Fabulous: The Movie | R | 22 | 7 | 2016 | 2016-07-22 | 91 | 5.4 | Jennifer Saunders | 9161 | Jennifer Saunders | 2016 | #AbsolutelyFabulous:TheMovie |
| 719 | 0.0 | Siempre Viva Productions | USA | Paul Duddridge | Drama | 28368.0 | Mothers and Daughters | PG-13 | 6 | 5 | 2016 | 2016-05-6 | 90 | 4.9 | Selma Blair | 1959 | Paige Cameron | 2016 | #MothersandDaughters |
| 720 | 3500000.0 | Warner Bros. Animation | USA | Sam Liu | Animation | 3775000.0 | Batman: The Killing Joke | R | 25 | 7 | 2016 | 2016-07-25 | 76 | 6.5 | Kevin Conroy | 36333 | Brian Azzarello | 2016 | #Batman:TheKillingJoke |
| 721 | 0.0 | Borderline Presents | USA | Nicolas Pesce | Drama | 25981.0 | The Eyes of My Mother | R | 2 | 12 | 2016 | 2016-12-2 | 76 | 6.2 | Kika Magalhães | 6947 | Nicolas Pesce | 2016 | #TheEyesofMyMother |
| 722 | 0.0 | Les Productions du Trésor | France | Nicole Garcia | Drama | 37757.0 | From the Land of the Moon | R | 28 | 7 | 2017 | 2017-07-28 | 120 | 6.7 | Marion Cotillard | 2411 | Milena Agus | 2016 | #FromtheLandoftheMoon |
723 rows × 19 columns
Extraccion de twits ... ¡Atención! cada 25 peliculas esperar 15 minutos
In [ ]:
contador = 0
cadena_final = ""
for filas in datos_excel["hashtag"] :
hashtag=datos_excel["hashtag"][contador]
fecha=datos_excel["released"][contador]
if(contador % 25)== 0 and contador!= 0 :
import time
time.sleep(755)
cadena_texto= extraer_tweet(fecha, hashtag)
print(f"Con demoras :{hashtag}")
else:
print(f"Sin demoras :{hashtag}")
cadena_texto= extraer_tweet(fecha, hashtag)
cadena_final+=cadena_texto
escribir_archivo(cadena_final)
contador+=1
Sin demoras :#TheWolfofWallStreet Sin demoras :#TheConjuring Sin demoras :#Prisoners Sin demoras :#Frozen Sin demoras :#NowYouSeeMe Sin demoras :#Her Sin demoras :#ManofSteel Sin demoras :#We'retheMillers Sin demoras :#BlueIstheWarmestColor Sin demoras :#PacificRim Sin demoras :#TheHungerGames:CatchingFire Sin demoras :#AmericanHustle Sin demoras :#Gravity Sin demoras :#12YearsaSlave Sin demoras :#StarTrekIntoDarkness Sin demoras :#OlympusHasFallen Sin demoras :#Oblivion Sin demoras :#DallasBuyersClub Sin demoras :#AboutTime Sin demoras :#WorldWarZ Sin demoras :#GangsterSquad Sin demoras :#LoneSurvivor Sin demoras :#Carrie Sin demoras :#Thor:TheDarkWorld Sin demoras :#Rush Con demoras :#ThisIstheEnd Sin demoras :#Lanochedelaexpiación Sin demoras :#Fast&Furious6 Sin demoras :#TheGreenInferno Sin demoras :#GrownUps2 Sin demoras :#Kick-Ass2 Sin demoras :#TheHobbit:TheDesolationofSmaug Sin demoras :#BeautifulCreatures Sin demoras :#TheCounsellor Sin demoras :#EvilDead Sin demoras :#TheSecretLifeofWalterMitty Sin demoras :#Elysium Sin demoras :#TheWorld'sEnd Sin demoras :#TheHeat Sin demoras :#TheFamily Sin demoras :#UndertheSkin Sin demoras :#DespicableMe2 Sin demoras :#DonJon Sin demoras :#TheMortalInstruments:CityofBones Sin demoras :#OldBoy Sin demoras :#SafeHaven Sin demoras :#G.I.Joe:Retaliation Sin demoras :#Movie43 Sin demoras :#EscapePlan Sin demoras :#Ender'sGame Con demoras :#OutoftheFurnace Sin demoras :#Oculus Sin demoras :#Pain&Gain Sin demoras :#Stoker Sin demoras :#TheCroods Sin demoras :#TheLastStand Sin demoras :#Coherence Sin demoras :#TheWolverine Sin demoras :#WarmBodies Sin demoras :#TexasChainsaw3D Sin demoras :#CaptainPhillips Sin demoras :#TheSpectacularNow Sin demoras :#TheHangoverPartIII Sin demoras :#TheInternship Sin demoras :#Horns Sin demoras :#TheLoneRanger Sin demoras :#OztheGreatandPowerful Sin demoras :#MonstersUniversity Sin demoras :#WhiteHouseDown Sin demoras :#Insidious:Chapter2 Sin demoras :#SideEffects Sin demoras :#PercyJackson:SeaofMonsters Sin demoras :#Locke Sin demoras :#RED2 Sin demoras :#BeginAgain
Función para extraer twits
In [200]:
def extraer_tweet(fecha, hashtag) :
cadena=""
for tweet in tweepy.Cursor(api.search, q=hashtag, tweet_mode ="extended", locale="en", count= 5 , since=fecha).items(40):
#print (tweet.full_text) created_at metadata.
text_tweet= tweet._json["full_text"]
date_tweet= tweet._json["created_at"]
text_tweet= pre_procesado(text_tweet)
cadena+=hashtag+"|"+text_tweet+"|"+"|"+date_since+"\n"
return cadena
Función para escribir twiter plano
In [218]:
def escribir_archivo(cadena_final) :
g = open("C:/Users/ASUS/Downloads/destino.csv","w")
g.write(cadena_final)
g.close()
In [216]:
g = open("C:/Users/ASUS/Downloads/destino.csv","w")
g.write(cadena_final)
g.close()
Funcion para cargar corpus de embedding en inglés para tener datos de contraste semántico
In [22]:
def cargar_archivo(filename):
dic = {}
with open(filename, encoding='utf-8') as archivo:
for linea in archivo:
vec = linea.split()
palabra = vec[0]
vec_num = np.array(vec[1:], dtype=float)
dic[palabra] = vec_num
return dic
Realizamos el cargue corpus
In [23]:
%%time
sbw = cargar_archivo('glove.6B.100d.txt')
sbw
Wall time: 33.3 s
Out[23]:
{'the': array([-0.038194, -0.24487 , 0.72812 , -0.39961 , 0.083172, 0.043953,
-0.39141 , 0.3344 , -0.57545 , 0.087459, 0.28787 , -0.06731 ,
0.30906 , -0.26384 , -0.13231 , -0.20757 , 0.33395 , -0.33848 ,
-0.31743 , -0.48336 , 0.1464 , -0.37304 , 0.34577 , 0.052041,
0.44946 , -0.46971 , 0.02628 , -0.54155 , -0.15518 , -0.14107 ,
-0.039722, 0.28277 , 0.14393 , 0.23464 , -0.31021 , 0.086173,
0.20397 , 0.52624 , 0.17164 , -0.082378, -0.71787 , -0.41531 ,
0.20335 , -0.12763 , 0.41367 , 0.55187 , 0.57908 , -0.33477 ,
-0.36559 , -0.54857 , -0.062892, 0.26584 , 0.30205 , 0.99775 ,
-0.80481 , -3.0243 , 0.01254 , -0.36942 , 2.2167 , 0.72201 ,
-0.24978 , 0.92136 , 0.034514, 0.46745 , 1.1079 , -0.19358 ,
-0.074575, 0.23353 , -0.052062, -0.22044 , 0.057162, -0.15806 ,
-0.30798 , -0.41625 , 0.37972 , 0.15006 , -0.53212 , -0.2055 ,
-1.2526 , 0.071624, 0.70565 , 0.49744 , -0.42063 , 0.26148 ,
-1.538 , -0.30223 , -0.073438, -0.28312 , 0.37104 , -0.25217 ,
0.016215, -0.017099, -0.38984 , 0.87424 , -0.72569 , -0.51058 ,
-0.52028 , -0.1459 , 0.8278 , 0.27062 ]),
',': array([-0.10767 , 0.11053 , 0.59812 , -0.54361 , 0.67396 ,
0.10663 , 0.038867 , 0.35481 , 0.06351 , -0.094189 ,
0.15786 , -0.81665 , 0.14172 , 0.21939 , 0.58505 ,
-0.52158 , 0.22783 , -0.16642 , -0.68228 , 0.3587 ,
0.42568 , 0.19021 , 0.91963 , 0.57555 , 0.46185 ,
0.42363 , -0.095399 , -0.42749 , -0.16567 , -0.056842 ,
-0.29595 , 0.26037 , -0.26606 , -0.070404 , -0.27662 ,
0.15821 , 0.69825 , 0.43081 , 0.27952 , -0.45437 ,
-0.33801 , -0.58184 , 0.22364 , -0.5778 , -0.26862 ,
-0.20425 , 0.56394 , -0.58524 , -0.14365 , -0.64218 ,
0.0054697, -0.35248 , 0.16162 , 1.1796 , -0.47674 ,
-2.7553 , -0.1321 , -0.047729 , 1.0655 , 1.1034 ,
-0.2208 , 0.18669 , 0.13177 , 0.15117 , 0.7131 ,
-0.35215 , 0.91348 , 0.61783 , 0.70992 , 0.23955 ,
-0.14571 , -0.37859 , -0.045959 , -0.47368 , 0.2385 ,
0.20536 , -0.18996 , 0.32507 , -1.1112 , -0.36341 ,
0.98679 , -0.084776 , -0.54008 , 0.11726 , -1.0194 ,
-0.24424 , 0.12771 , 0.013884 , 0.080374 , -0.35414 ,
0.34951 , -0.7226 , 0.37549 , 0.4441 , -0.99059 ,
0.61214 , -0.35111 , -0.83155 , 0.45293 , 0.082577 ]),
'.': array([-0.33979 , 0.20941 , 0.46348 , -0.64792 , -0.38377 ,
0.038034 , 0.17127 , 0.15978 , 0.46619 , -0.019169 ,
0.41479 , -0.34349 , 0.26872 , 0.04464 , 0.42131 ,
-0.41032 , 0.15459 , 0.022239 , -0.64653 , 0.25256 ,
0.043136 , -0.19445 , 0.46516 , 0.45651 , 0.68588 ,
0.091295 , 0.21875 , -0.70351 , 0.16785 , -0.35079 ,
-0.12634 , 0.66384 , -0.2582 , 0.036542 , -0.13605 ,
0.40253 , 0.14289 , 0.38132 , -0.12283 , -0.45886 ,
-0.25282 , -0.30432 , -0.11215 , -0.26182 , -0.22482 ,
-0.44554 , 0.2991 , -0.85612 , -0.14503 , -0.49086 ,
0.0082973, -0.17491 , 0.27524 , 1.4401 , -0.21239 ,
-2.8435 , -0.27958 , -0.45722 , 1.6386 , 0.78808 ,
-0.55262 , 0.65 , 0.086426 , 0.39012 , 1.0632 ,
-0.35379 , 0.48328 , 0.346 , 0.84174 , 0.098707 ,
-0.24213 , -0.27053 , 0.045287 , -0.40147 , 0.11395 ,
0.0062226, 0.036673 , 0.018518 , -1.0213 , -0.20806 ,
0.64072 , -0.068763 , -0.58635 , 0.33476 , -1.1432 ,
-0.1148 , -0.25091 , -0.45907 , -0.096819 , -0.17946 ,
-0.063351 , -0.67412 , -0.068895 , 0.53604 , -0.87773 ,
0.31802 , -0.39242 , -0.23394 , 0.47298 , -0.028803 ]),
'of': array([-0.1529 , -0.24279 , 0.89837 , 0.16996 , 0.53516 , 0.48784 ,
-0.58826 , -0.17982 , -1.3581 , 0.42541 , 0.15377 , 0.24215 ,
0.13474 , 0.41193 , 0.67043 , -0.56418 , 0.42985 , -0.012183,
-0.11677 , 0.31781 , 0.054177, -0.054273, 0.35516 , -0.30241 ,
0.31434 , -0.33846 , 0.71715 , -0.26855 , -0.15837 , -0.47467 ,
0.051581, -0.33252 , 0.15003 , -0.1299 , -0.54617 , -0.37843 ,
0.64261 , 0.82187 , -0.080006, 0.078479, -0.96976 , -0.57741 ,
0.56491 , -0.39873 , -0.057099, 0.19743 , 0.065706, -0.48092 ,
-0.20125 , -0.40834 , 0.39456 , -0.02642 , -0.11838 , 1.012 ,
-0.53171 , -2.7474 , -0.042981, -0.74849 , 1.7574 , 0.59085 ,
0.04885 , 0.78267 , 0.38497 , 0.42097 , 0.67882 , 0.10337 ,
0.6328 , -0.026595, 0.58647 , -0.44332 , 0.33057 , -0.12022 ,
-0.55645 , 0.073611, 0.20915 , 0.43395 , -0.012761, 0.089874,
-1.7991 , 0.084808, 0.77112 , 0.63105 , -0.90685 , 0.60326 ,
-1.7515 , 0.18596 , -0.50687 , -0.70203 , 0.66578 , -0.81304 ,
0.18712 , -0.018488, -0.26757 , 0.727 , -0.59363 , -0.34839 ,
-0.56094 , -0.591 , 1.0039 , 0.20664 ]),
'to': array([-1.8970e-01, 5.0024e-02, 1.9084e-01, -4.9184e-02, -8.9737e-02,
2.1006e-01, -5.4952e-01, 9.8377e-02, -2.0135e-01, 3.4241e-01,
-9.2677e-02, 1.6100e-01, -1.3268e-01, -2.8160e-01, 1.8737e-01,
-4.2959e-01, 9.6039e-01, 1.3972e-01, -1.0781e+00, 4.0518e-01,
5.0539e-01, -5.5064e-01, 4.8440e-01, 3.8044e-01, -2.9055e-03,
-3.4942e-01, -9.9696e-02, -7.8368e-01, 1.0363e+00, -2.3140e-01,
-4.7121e-01, 5.7126e-01, -2.1454e-01, 3.5958e-01, -4.8319e-01,
1.0875e+00, 2.8524e-01, 1.2447e-01, -3.9248e-02, -7.6732e-02,
-7.6343e-01, -3.2409e-01, -5.7490e-01, -1.0893e+00, -4.1811e-01,
4.5120e-01, 1.2112e-01, -5.1367e-01, -1.3349e-01, -1.1378e+00,
-2.8768e-01, 1.6774e-01, 5.5804e-01, 1.5387e+00, 1.8859e-02,
-2.9721e+00, -2.4216e-01, -9.2495e-01, 2.1992e+00, 2.8234e-01,
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0.74597 , 0.024362, 0.50921 , 0.14636 , 0.56209 , -0.51742 ,
0.4382 , 0.49828 , 0.22301 , -1.4558 , 0.087331, 1.7596 ,
-0.37007 , -1.3982 , -0.016184, -1.0059 , 1.6228 , 0.64449 ,
-0.608 , 0.14222 , -0.75322 , -1.0962 , 0.68042 , 0.019071,
0.49995 , 0.093304, -0.38055 , 1.2097 , 0.038779, -0.20505 ,
-0.090177, 0.10075 , -0.079859, 0.45471 , 0.5807 , -0.05621 ,
-0.38867 , 0.53159 , 0.89746 , 0.13347 , -0.21169 , 0.2026 ,
-1.5201 , 0.63185 , 0.20985 , -0.20975 , 0.28428 , -0.051424,
0.55961 , -1.1181 , 0.17657 , -0.43124 , 0.33481 , 0.26021 ,
0.66202 , 0.1818 , 0.53301 , -0.76157 ]),
'feel': array([-4.2895e-02, 7.0671e-01, 7.5316e-01, -5.9963e-01, -8.0169e-01,
6.0094e-01, -6.7419e-01, 2.3592e-01, 2.4005e-01, -3.6372e-01,
-2.2720e-01, 3.6026e-02, 4.6591e-01, -3.6233e-01, -3.1325e-01,
-3.6757e-01, -5.7255e-01, 3.0661e-01, -4.8606e-01, 6.3214e-01,
2.8931e-01, 6.0741e-01, -8.6788e-03, -6.8206e-01, -1.8410e-01,
2.4847e-01, 9.5902e-02, -6.9108e-01, 9.0869e-01, -3.9224e-01,
1.4345e-01, 7.8112e-01, -1.1601e-01, 6.0312e-02, 4.6300e-01,
-4.4191e-02, 1.4284e-01, 6.2234e-01, 2.3943e-01, -4.6815e-01,
-2.2553e-01, -3.1920e-01, 3.1397e-01, -4.2308e-01, -4.0827e-01,
-2.4659e-01, 5.1572e-01, 3.5494e-01, 3.1545e-01, -1.4043e+00,
-1.6486e-01, -5.5508e-02, 1.0260e-01, 6.1515e-01, 1.2691e-01,
-2.2379e+00, 6.9510e-01, 1.2609e-01, 1.0901e+00, 2.5953e-01,
2.2974e-01, 1.1125e+00, -1.0156e+00, -4.1881e-01, 3.8853e-01,
-4.8856e-02, 9.1775e-01, 1.9732e-03, -6.9162e-01, -5.8945e-01,
3.5722e-01, -5.2180e-01, -1.2020e-01, -1.3826e-01, 2.1568e-01,
5.7880e-01, 3.7060e-02, -5.7264e-02, -2.7097e-02, -2.5526e-01,
-3.9219e-01, 3.7948e-02, -8.5041e-01, 4.6354e-01, -1.6462e+00,
-2.7176e-01, -1.8171e-01, -3.8026e-02, -9.7623e-01, -5.1404e-01,
6.9445e-02, -4.6013e-01, 2.5073e-01, -5.0782e-01, -1.5645e-01,
1.2076e-01, -5.5868e-02, -5.4230e-01, 1.1809e-01, 3.9974e-01]),
'attention': array([-3.3414e-01, 4.6667e-01, 5.3744e-01, 5.7743e-02, 2.9642e-01,
2.5224e-01, -6.5586e-01, -4.1668e-01, 2.1959e-01, -4.9413e-01,
-2.1816e-01, -9.0227e-02, -3.5179e-02, -2.7279e-01, -1.2343e-01,
1.6808e-01, -5.0623e-01, -4.0497e-01, -1.6763e-01, 4.9066e-01,
-8.8020e-02, -1.2339e-01, -3.8436e-01, -2.7766e-01, -1.3403e-01,
1.4342e-01, -2.9177e-01, -2.1146e-02, 5.2180e-01, -2.1213e-01,
3.0860e-02, 1.0402e-01, -1.6807e-01, 4.6170e-01, -5.4806e-01,
-6.6849e-02, -3.3180e-01, 3.7257e-01, -7.4962e-01, 6.2741e-01,
-4.9500e-01, -4.0996e-01, -1.4686e-01, -2.7166e-01, -7.7093e-02,
-2.8342e-01, 6.3663e-02, -1.5734e-01, 6.9649e-01, -9.6694e-01,
4.4510e-01, -2.4521e-01, -4.8447e-01, 1.1957e+00, 2.9929e-02,
-2.0425e+00, -2.8603e-01, -3.9043e-01, 1.2197e+00, -4.7760e-01,
-2.1191e-02, 9.3080e-01, -1.8173e-01, -7.5721e-02, 1.1242e+00,
-8.2276e-02, 5.7149e-02, -2.3585e-01, 3.5901e-01, 6.9223e-01,
3.0860e-02, -2.6091e-01, -3.0284e-01, -3.9094e-01, 2.1887e-01,
3.7618e-01, -1.5990e-01, -2.7495e-01, -8.3322e-01, -1.7576e-01,
5.7404e-01, -3.6228e-01, 2.2346e-01, -4.6975e-01, -1.4354e+00,
-2.4484e-01, -6.6958e-01, -5.4293e-03, -5.7340e-01, -6.3122e-01,
9.8732e-03, -1.9317e-01, 3.0410e-01, -2.6329e-01, -1.0366e-03,
3.6515e-01, -9.3829e-02, -6.7265e-02, 5.9425e-01, 6.7652e-01]),
...}
In [24]:
type(sbw)
Out[24]:
dict
Se crea el arreglo con las palabras de twiter con el verctor del corpus
In [25]:
arreglo_fila={}
arreglo_fila['brave'] = sbw['brave']
arreglo_fila[ 'tweet'] = sbw['tweet']
arreglo_fila[ '#oblivion'] = sbw['oblivion']
arreglo_fila[ 'is'] = sbw['is']
arreglo_fila[ 'better'] = sbw['better']
arreglo_fila[ 'than'] = sbw['than']
#arreglo_fila[ 'skyrim'] = sbw['skyrim']
arreglo_fila[ 'in'] = sbw['in']
arreglo_fila[ 'every'] = sbw['every']
arreglo_fila[ 'way'] = sbw['way']
arreglo_fila[ 'excluding'] = sbw['excluding']
arreglo_fila[ 'the'] = sbw['the']
arreglo_fila[ 'ability'] = sbw['ability']
arreglo_fila[ 'to'] = sbw['to']
arreglo_fila[ 'forge'] = sbw['forge']
arreglo_fila[ 'weapons'] = sbw['weapons']
arreglo_fila[ 'and'] = sbw['and']
arreglo_fila[ 'armor'] = sbw['armor']
arreglo_fila[ '#oblivion'] = sbw['oblivion']
#arreglo_fila[ '#skyrim'] = sbw['skyrim']
#arreglo_fila[ '#elderscrolls'] = sbw['elderscrolls']
#sbw.index("they")
Se genera la función de palabras cercanas
In [7]:
def palabras_cercanas(embedding, palabra, n=5):
distances = []
for key, value in embedding.items():
dist = np.linalg.norm(embedding[palabra]-embedding[key])
distances.append((dist,key))
distances.sort()
resultado = [d[1] for d in distances[1:n+1]]
return resultado
Agregar las palabras cercanas a nuestra lematizacion
In [8]:
def busqueda_cercana(palabra):
for fila_palabra in palabra :
arreglo_fila2 = arreglo_fila
resultado = palabras_cercanas(sbw, fila_palabra, n=5)
for filas in resultado:
arreglo_fila2[ filas] = sbw[filas]
return arreglo_fila2
In [16]:
arreglo_palabras2=["brave","better","ability"]
arreglo_fila2=busqueda_cercana(arreglo_palabras2)
Transformacion de datos en matrix
In [12]:
matrix = pd.DataFrame(arreglo_fila)
matrix=matrix.T.round(3)
matrix
Out[12]:
| 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | ... | 90 | 91 | 92 | 93 | 94 | 95 | 96 | 97 | 98 | 99 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| brave | -0.311 | -0.025 | 0.069 | -0.440 | -0.412 | 0.616 | -0.522 | -0.250 | -0.992 | -0.371 | ... | 0.199 | -0.205 | 0.310 | 0.250 | -1.098 | -0.001 | -0.809 | -0.145 | -0.806 | 0.438 |
| tweet | -0.072 | 0.084 | 0.134 | -0.956 | 0.030 | 0.552 | 0.186 | -0.239 | 0.522 | 0.446 | ... | 0.069 | 0.354 | 0.180 | -0.185 | 0.779 | -0.258 | 0.156 | -0.154 | -0.693 | 0.135 |
| #oblivion | -0.130 | 0.018 | 0.896 | -0.260 | 0.309 | 0.621 | -0.165 | -0.499 | 0.342 | -0.228 | ... | 0.287 | 0.354 | -0.019 | 1.016 | 0.230 | -0.426 | 0.454 | 0.598 | -0.558 | -0.293 |
| is | -0.543 | 0.415 | 1.032 | -0.402 | 0.467 | 0.218 | -0.075 | 0.473 | 0.081 | -0.221 | ... | -0.325 | -0.135 | -0.413 | 0.334 | -0.007 | 0.323 | -0.044 | -1.297 | 0.762 | 0.463 |
| better | -0.048 | 0.519 | 0.343 | -0.096 | -0.447 | -0.371 | -0.129 | -0.503 | -0.261 | -0.091 | ... | 0.418 | -0.319 | 0.109 | -0.436 | -0.545 | 0.075 | -0.170 | -0.269 | 0.487 | 0.556 |
| than | 0.103 | 1.247 | 0.567 | -0.191 | -0.336 | 0.434 | 0.131 | 0.109 | -0.304 | 0.272 | ... | 0.509 | 0.112 | -0.176 | -0.509 | -0.941 | -0.054 | -0.570 | -0.258 | 0.817 | 0.090 |
| in | 0.086 | -0.222 | 0.166 | 0.134 | 0.382 | 0.354 | 0.013 | 0.225 | -0.438 | 0.502 | ... | 0.402 | -0.127 | -0.202 | 0.874 | -0.545 | 0.792 | -0.207 | -0.074 | 0.758 | -0.342 |
| every | -0.277 | 0.927 | 0.173 | -0.045 | -0.083 | 1.046 | 0.217 | 0.875 | 0.036 | -0.159 | ... | -0.149 | 0.429 | 0.126 | -0.325 | -0.955 | -0.012 | -0.158 | 0.188 | 0.632 | 0.293 |
| way | 0.155 | 0.223 | 0.667 | -0.105 | -0.131 | 0.374 | -0.171 | 0.009 | 0.076 | -0.113 | ... | -0.292 | -0.319 | -0.251 | 0.086 | -0.502 | -0.150 | -0.078 | -0.252 | 0.388 | 0.835 |
| excluding | 0.017 | 0.046 | 0.574 | 0.318 | 0.385 | -0.811 | -0.143 | 0.277 | -0.037 | -0.148 | ... | -0.474 | -0.127 | -0.050 | -0.827 | -0.787 | -0.289 | 0.352 | 0.187 | 1.047 | -0.387 |
| the | -0.038 | -0.245 | 0.728 | -0.400 | 0.083 | 0.044 | -0.391 | 0.334 | -0.575 | 0.087 | ... | 0.016 | -0.017 | -0.390 | 0.874 | -0.726 | -0.511 | -0.520 | -0.146 | 0.828 | 0.271 |
| ability | -0.013 | -0.139 | 0.229 | 0.188 | -0.557 | -0.208 | -0.908 | -0.548 | -0.073 | 0.080 | ... | -0.486 | 0.122 | 0.088 | -0.237 | -0.284 | -0.020 | -0.189 | -1.144 | 0.766 | 0.468 |
| to | -0.190 | 0.050 | 0.191 | -0.049 | -0.090 | 0.210 | -0.550 | 0.098 | -0.201 | 0.342 | ... | -0.131 | 0.059 | -0.319 | -0.614 | -0.624 | -0.415 | -0.038 | -0.398 | 0.476 | -0.160 |
| forge | 0.076 | 0.182 | -0.368 | 0.503 | 0.419 | -1.119 | -0.150 | -0.582 | 0.422 | 0.185 | ... | -0.442 | 0.164 | -0.539 | -0.096 | -0.257 | -0.377 | 0.106 | -0.384 | 0.504 | 0.335 |
| weapons | 0.335 | 0.705 | 0.709 | -0.390 | 0.065 | -0.822 | 0.514 | 0.329 | 0.800 | 1.502 | ... | -1.094 | -0.017 | 0.558 | 0.215 | -0.860 | -0.206 | -0.490 | -0.103 | 0.991 | 0.342 |
| and | -0.072 | 0.231 | 0.024 | -0.506 | 0.339 | 0.196 | -0.329 | 0.184 | -0.181 | 0.290 | ... | 0.014 | -0.626 | -0.324 | 0.419 | -1.081 | 0.467 | -0.499 | -0.719 | 0.869 | 0.195 |
| armor | -0.649 | 0.389 | 0.277 | -0.178 | -0.095 | -0.211 | 0.045 | -0.247 | 0.184 | 1.439 | ... | 0.143 | -0.899 | 0.191 | 0.322 | 0.024 | -0.083 | 0.041 | -0.524 | 0.422 | 0.091 |
17 rows × 100 columns
Reduccion de dimensionalidad para proceder al análisis
In [13]:
from sklearn.decomposition import PCA
import matplotlib.pyplot as plt
import plotly.graph_objs as go
from plotly.offline import iplot
In [14]:
pca = PCA(n_components=2)
result = pca.fit_transform(matrix)
result = pd.DataFrame(result)
result.columns = ['X', 'Y']
result['Palabra'] = matrix.index.values
result
Out[14]:
| X | Y | Palabra | |
|---|---|---|---|
| 0 | 2.220902 | -1.405540 | brave |
| 1 | 4.042704 | -2.674299 | tweet |
| 2 | 3.111780 | -1.483440 | #oblivion |
| 3 | -2.009358 | -0.662533 | is |
| 4 | -1.148198 | -0.619154 | better |
| 5 | -1.830755 | -0.377632 | than |
| 6 | -2.194233 | -0.470132 | in |
| 7 | -1.308450 | -1.355635 | every |
| 8 | -1.235898 | -0.693307 | way |
| 9 | 1.027991 | 0.186320 | excluding |
| 10 | -1.892304 | -0.093452 | the |
| 11 | -0.119786 | 0.731300 | ability |
| 12 | -2.016273 | -0.233841 | to |
| 13 | 1.772669 | 0.946673 | forge |
| 14 | 0.424078 | 4.627143 | weapons |
| 15 | -1.570974 | 0.463577 | and |
| 16 | 2.726106 | 3.113952 | armor |
In [15]:
trace = go.Scatter(x=result['X'].values,
y=result['Y'].values,
text=result['Palabra'].values,
mode='markers')
layout = go.Layout(title="PCA")
fig = go.Figure(data=trace, layout=layout)
iplot(fig)
Esta primera visualización nos permite encontrar que en las coordenadas de (-1 y 1) se agrupan los conectores lógicos de tiempo, espacio o que indican la propiedad de un objeto o sujeto (to, in, the, is...) Estas palabras tienen la caracterÃsticas de que no son reemplazables o no tienen sinónimos puesto que sintácticamente tienen funciones lingüÃsticas definidas inamovibles.
El resto de la gráfica nos muestra palabras que están muy lejanas. Sorprende la distancia entre (weapons y armor); sin embargo esto podrÃa explicarse por la falta de contexto semántico con la que cuenta esta primera visualización.
In [17]:
matrix2 = pd.DataFrame(arreglo_fila2)
matrix2=matrix2.T.round(3)
result2 = pca.fit_transform(matrix2)
result2 = pd.DataFrame(result2)
result2.columns = ['X', 'Y']
result2['Palabra'] = matrix2.index.values
trace = go.Scatter(x=result2['X'].values,
y=result2['Y'].values,
text=result2['Palabra'].values,
mode='markers')
layout = go.Layout(title="PCA")
fig = go.Figure(data=trace, layout=layout)
iplot(fig)
Esta segunda gráfica, muestra una representación de cercanÃas mucho más completa. Podemos indicar que hay tres grupos que saltan a la vista:
- El de los conectores lógicos que describimos en la imagen anterior y que se mantiene en esta
- Caracterización contextual de los sustantivos ability e inability (eje x -1 y 2, eje y 0 y 4). Encontramos que Se presentan dos contextos de uso: ability en relación con weapons y armor e inability en relación con forge. Esta clasificación nos da una intuición de los sentimientos (positivos o negativos) frente a carcaterÃsticas de la pelÃcula cometnada (#Oblivion).
- La tercera hace referencia a la agrupación de adjetivos con los que se cualifica la pelÃcula y su relación: heroic, brave, fearlness, valient.
La impresión que nos deja este análisis preeliminar es que:
En un cotexto de uso semántico de "pelÃculas de acción" se empiezan a distinguir claramente unas tradiciones discursivas que, ene este punto se encuentran en los adjetivos calificativos.
Intentamos generar un embedding personalizado con la muestra de los twits recolectados¶
In [26]:
import re
import pandas as pd
from nltk.corpus import stopwords
stopwords_sp = stopwords.words('english')
def pre_procesado2_s(texto):
texto = texto.lower()
#texto = re.sub(r"[\W\d_]+", " ", texto)
texto = " ".join([palabra for palabra in texto.split() if palabra not in stopwords_sp])
return texto.split()
Utilizamos la función lamda que hemos venido usando en clase
In [27]:
datos_twiter=pd.read_csv("destino.csv",sep="|")
datos_twiter["text_tweet_2"] = datos_twiter["text_tweet"].apply(lambda texto: pre_procesado2_s(texto) )
datos_twiter.head()
Out[27]:
| hashtag | text_tweet | blanco | date | text_tweet_2 | |
|---|---|---|---|---|---|
| 0 | #TheWolfofWallStreet | we re not going to be friends https t co yy0v... | NaN | 2020-5-21 | [going, friends, https, co, yy0vzzyenh, przez,... |
| 1 | #TheWolfofWallStreet | #thewolfofwallstreet https t co 6ds8x2f22m | NaN | 2020-5-21 | [#thewolfofwallstreet, https, co, 6ds8x2f22m] |
| 2 | #TheWolfofWallStreet | when all goes well let em know amp thump t... | NaN | 2020-5-21 | [goes, well, let, em, know, amp, thump, chest,... |
| 3 | #TheWolfofWallStreet | rt @imdb imdb fan favorite movie that starts ... | NaN | 2020-5-21 | [rt, @imdb, imdb, fan, favorite, movie, starts... |
| 4 | #TheWolfofWallStreet | have you seen the wolf of wall street here... | NaN | 2020-5-21 | [seen, wolf, wall, street, scene, movie, teach... |
Cargamos las librerÃas para generar el modelo
In [28]:
import gensim.models.word2vec as w2v
import matplotlib.pyplot as plt
import plotly.graph_objs as go
from plotly.offline import iplot
In [29]:
datos_twiter['text_tweet_2'].values
Out[29]:
array([list(['going', 'friends', 'https', 'co', 'yy0vzzyenh', 'przez', '@youtube', '#margotrobbie', '#leonardodicaprio', '#thewolfofwallstreet']),
list(['#thewolfofwallstreet', 'https', 'co', '6ds8x2f22m']),
list(['goes', 'well', 'let', 'em', 'know', 'amp', 'thump', 'chest', '#thump', '#thewolfofwallstreet', 'https', 'co', 'k24a7ahgkv']),
...,
list(['#beginagain', '#saturdaythoughts', 'https', 'co', 'nkoze0dgda']),
list(['rt', '@maha_medd', 'map', 'prepared', 'csir', 'neeri', 'mumbai', 'showing', '#covid19', 'cases', 'india', 'amp', 'maharashtra', '19', '09', '2020', '#maharashtrafightscorona', '#']),
list(['everyone', 'past', 'present', 'future', 'growing', 'please', 'patient', 'love', '#like', '#follow', '#nohate', '#peaceandlove', '#makeamends', '#beginagain', 'https', 'co', 'lsf6vabpl6'])],
dtype=object)
Nuestro modelo sigue un Skip Gram: es decir queremos predecir el contexto a partir de la palabra (contexto de uso)¶
En este punto queremos hacer nuestro modelo usando todos los twits recolectados y no solo con una pelÃcula, como lo mostramos en el paso anterior. Escogimos el modelo de skip gram, es decir el que nos predice el contexto o palabras cercanas a partir de una palabra. A continuación presentamos el proceso:
In [30]:
%%time
mi_modelo = w2v.Word2Vec(datos_twiter['text_tweet_2'].values,
sg=400, # 1 skip-gram. 0 CBOW
seed=100, # semilla
##size=9999999, # número de dimensiones
#min_count=750,
#window=9999999
)
Wall time: 839 ms
Probando el modelo¶
Seleccionamos la palabra "friends" para establecer cuales son las palabras que rodean o que habitualmente acompañan el contexto de uso de esta palabra... los resultados fueron los siguientes....
In [31]:
vocab = [e[0] for e in mi_modelo.wv.most_similar("alpha")]+[e[0] for e in mi_modelo.wv.most_similar("friends")]
print(vocab)
['action', 'download', 'next', 'commission', 'eine', 'goes', 'thank', '#watercolor', 'lots', 'original', 'say', 'live', 'full', 'watch', 'everyone', '7', 'make', 'much', '#howtotrainyourdragon', 'overcome']
Nos nos sorprende que en el contexto semántico de la palabra "friends" se encuentren palabras como make, fettind, safehaven, neer. Algunas de ellas nos representan expresiones compuestas y otras contextos de usos cotidianos que en, efecto, nos permiten concluir que el modelo responde con buen grado de acierto...
Aunque nos parecieron buenos resultados y estamos felicies por tener un primer acercamiento desde las técnicas de NLP a nuestro datos, tenemos interrogantes sobre las palabras en francés que aparecen como "aimerait" o "sampai"... seguiremos haciendo ajustes... 
Queremos visualizar nuestros datos... ¿tendremos algunas pistas?¶
In [32]:
mi_modelo.wv.vocab.keys()
Out[32]:
dict_keys(['going', 'friends', 'https', 'co', '@youtube', '#leonardodicaprio', '#thewolfofwallstreet', 'goes', 'well', 'let', 'em', 'know', 'amp', 'rt', '@imdb', 'imdb', 'fan', 'favorite', 'movie', 'starts', 'letter', 'w', '#moviesatoz', 'ymz63d3ilo', 'p', 'seen', 'wolf', 'wall', 'street', 'scene', '#movies', 'de', 'se', 'que', 'filme', 'la', 'del', 'es', 'lo', 'mejor', 'en', 'tiempo', '@tonytolj', 'el', 'lobo', 'como', '#film', 'take', 'game', 'white', 'follow', 'us', 'download', 'gt', 'um', 'n', 'release', 'please', '20', '15', 'wir', 'das', 'mal', 'auf', 'eine', 'mit', 'dem', 'film', 'den', '3', 'para', 'always', '10', 'things', 'real', 'veces', 'un', '#theconjuring', '@thisdayinmetal0', 'sept', '19th', '1986', '#megadeth', 'released', 'album', 'peace', 'sells', 'buying', '#badomen', '#wakeupdead', '#mylastwords', '#theco', 'favourite', '2', 'da', 'una', '4', 'por', 'su', 'esta', 'v', 'big', 'house', 'one', 'ever', 'watch', '2019', '#b', 'still', 'believe', 'back', 'next', 'year', '#halloween', '#michaelmyers', '#horror', '19', 'os', '#', 'part', 'almost', 'made', 'shit', 'entire', '@tarizsolis', 'dari', 'dwilogi', 'sampai', '#pengabdisetan', 'inilah', '25', 'horor', 'paling', 'menyeramkan', 'dalam', 'sepuluh', 'tahun', 'terakhir', 'vers', 'order', 'online', 'sure', 'gonna', 'sleep', 'good', 'tonight', '@secretprisoner', '#prisoners', 'families', 'forgotten', 'collateral', 'sentence', 'handed', 'defendant', 'damage', 'relati', 'little', 'known', '7', 'con', 'jake', 'gyllenhaal', 'playing', 'left', '@onlyfilmmedia', '#todayinmoviehistory', 'september', '2013', '7th', 'anniversary', 'keller', 'dover', 'daughter', 'friend', 'go', '@indiewire', 'looks', 'deakins', 'villeneuve', 'helped', 'overcome', 'impossible', 'hd', 'even', '#covid19', 'song', 'free', 'stop', 'people', 'et', 'avant', 'je', 'bien', 'r', 'qu', 'une', 'ou', 'non', 'le', 'des', 'pas', 'prison', 'action', 'takes', 'cast', 'moment', 'really', 'see', 'something', 'story', 'try', 'needs', '#nowyouseeme', 'find', 'call', 'sequel', 'opportunity', 'digital', 'stay', 'thriller', '09', '00pm', '9pm', '#todaysmovies', '5', '21', '00', '#5star', '#film4', '#sony', '30', 'way', 'u', 'dans', 'watching', '@nysmmovie', 'look', 'first', 'eye', 'calls', 'answer', 'upmom6iorr', 'movies', '#her', 'looking', 'pretty', 'put', 'world', '_', '2020', 'original', 'music', 'j', 'far', 'e', 'bitter', 'better', 'like', 'never', 'get', 'happy', 'sunday', 'morning', 'today', 'great', 'pray', '@vfsreviews', 'boom', 'giving', 'away', 'copy', '#supermanmot', 'buddy', '@riprocketpix', 'chance', 'win', 'winner', '#dccomics', '#manofsteel', 'time', '#henrycavill', '#zacksnyder', '#justiceleague', '#superman', '#illustration', 'z', '#music', 'skin', 'earth', 'gravity', 'yet', 'could', 'show', 'foundation', 'character', 'super', 'also', 'otherwise', 'superman', 'would', 'man', 'maybe', 'life', 'blue', 'boy', 'podcast', 'thanks', 'come', 'via', 'best', 'link', '#fanart', '#artist', '#art', 'got', 'video', 'use', 'version', 'pour', 'films', 'culture', 'hope', 'starting', '1', 'read', '#blueisthewarmestcolor', 'visit', 'forest', '#illustrationartists', '#myart', 'day', '#pacificrim', 'said', 'netflix', '#drawing', 'okay', 'place', 'feel', 'anyone', 'many', 'vs', '@matiasbergara', 'cherno', 'alpha', 'commission', 'colossal', '@danielwarrenart', '#watercolor', '8su1gvuich', 'course', 'set', 'say', 'celebrates', 'birthday', '#scifi', '#fantasy', 'l', 'huge', 'think', 'actually', 'make', 'much', 'awesome', 'getting', 'prequel', 'saw', 'stars', 'check', 'makes', 'final', 'jap', 'est', 'los', 'love', 'fire', 'excited', '#thehungergames', '#catchingfire', 'another', 'games', 'ya', 'las', '#americanhustle', 'coming', 'october', 'beat', 'voter', 'suppression', '#security', 'quest', 'russian', '#gravity', '#netflix', 'starring', 'space', '@diebo37', '#space', '#science', 'help', '#golf', 'full', 'new', 'black', 'enough', 'pandemic', 'keep', 'last', 'week', 'hard', 'holding', 'around', 'change', '2012', 'home', 'pel', 'cula', 'culas', 'estado', 'oscar', '#12yearsaslave', '@thecut', 'rising', 'given', 'making', 'sabotage', 'fear', 'tha', 'bad', 'says', 'side', '#freebritney', '12', 'years', 'finally', 'found', 'thank', 'right', 'till', 'night', 'woman', 'based', 'true', '@startrek', 'darkness', '#startrek', '#startrekintodarkness', 'may', 'tv', 'pleasure', '18', '50', 'trump', '#olympushasfallen', '@netflix', '#oblivion', 'actor', 'tech', 'lost', 'family', 'every', 'forget', 'oblivion', '#twitch', '#elderscrolls', '#skyrim', 'play', '#gamer', '#twitchstreamer', '#twitchtv', 'hi', '#xboxshare', '@aggie_do', '@bethesda', '@elderscrolls', 'must', 'beautiful', 'wow', 'available', 'c', 'live', 'working', '#steam', '#dallasbuyersclub', 'club', 'solo', 'der', 'und', 'noch', 'tengo', 'vida', 'quiero', 'otro', 'todo', 'te', 'desde', 'per', 'il', 'di', 'ne', '24', 'minutes', '#abouttime', 'evil', 'ser', 'work', '@', 'weekend', '#neverquit', 'listening', 'son', 'list', '@realdonaldtrump', 'old', 'want', '#wwz', '#worldwarz', '@terrafossil', 'seeds', 'dead', 'zombie', 'mayhem', 'gallows', 'humor', 'lots', 'amazon', 'h8nzryjhyl', '#zombies', '#sur', 'war', '#nowwatching', 'save', 'end', '@yu_cec', 'cool', 'pc', '23', 'http', '@zwiesinn', 'zu', 'wird', '#bombflix', '#gangstersquad', 'einen', 'h', 'took', 'die', 'finish', '#drive', '#lonesurvivor', 'small', '200', 'lone', 'survivor', 'event', 'virtual', 'join', 'guns', 'pain', 'du', '#carrie', '@ginap0rter', 'eyes', 'a9qbcpdacm', 'resides', 'role', 'al', '6', '#tsai', 'ing', 'wen', 'hate', 'chose', 'head', '#rush', 'everyone', '#thisistheend', 'thought', 'listen', 'need', 'add', '#happybirthday', 'este', 'ahora', 'sa', 'main', 'buy', 'ammo', '@bspodnetwork', '@lukewarmcinema', '#bspn', 'mcjqmbpcls', 'qui', 'played', 'ready', 'thing', 'hello', '@ktysklind', '#bloodysunday', '#mutantfam', '#horrorfam', '#thegreeninferno', 'used', 'kids', 'cannibals', 'hand', 'models', 'finger', 'licking', 'wrong', 'park', 'wild', 'hbo', 'green', 'inferno', 'viewer', 'discretion', 'give', '#grownups2', '#beautifulcreatures', 'times', 'tomorrow', 'plan', '100', 'reading', 'loved', 'series', 'book', '@emmyrossum', '@samlirro', '#ashwilliams', '#evildead', '#nurse', '#silenthill', 'prspfxyuok', 'channel', 'soir', 'evildead', 'single', 'lyric', 'travel', 'les', '#thesecretlifeofwaltermitty', 'ben', 'mi', '11', '#elysium', 'trip', 'send', 'fellow', '#theheat', '9', 'though', 'since', 'weekly', '@cruellaisdevine', '@robletofire', '@gottalaff', '#councilfornationalpolicy', '#kochnetwork', '#thefamily', '#shadownetwork', '#thepowerworshippers', '@senatorcollins', '@susan_hennessey', 'susan', 'manipulating', 'word', '#coun', 'ends', 'aussi', 'sur', '#discount', '@ends_famly', 'unisex', 'hoodie', 'pn9whrrnfd', '#shop', '#drip', '#newarrival', '#culture', '#onlineshop', '#su', '#summerfashion', '#summerdeals', 'apron', 'nbljbkjh2u', '@rustyrockets', '#undertheskin', '#yogi', '#master', 'view', 'poster', 'post', 'box', 'excellent', '@adamleealter', '@joerogan', '#scarlettjohansson', '#despicableme2', '#donjon', 'account', 'level', 'pass', 'avec', 'het', 'open', '#oldboy', 'g', '@kingkuinsan', 'kind', '#thedarkknightrises', 'si', 'sans', 'vet', 'news', 'sha', '#safehaven', '@safehavenio', '@traveltrademv', '#maldives', '#visitmaldivesnow', 'private', 'keys', '@inheriti_com', 'solution', 'joe', 'retaliation', '#escapeplan', '@eaglegryphon', 'escape', '@vitallacerda', '#vitallacerda', '#eaglegryphongames', '#boardgames', '@mississaugafes', 'grinds', 'gears', 'smoke', 'alarms', '#haveaplan', '#workingalarmssavel', 'trailer', 'oculus', 'vr', 'headset', '#oculus', '#vr', '64gb', 'reality', '@bakshiharsh55', 'helicopter', 'pjzm96skop', '#ue4', '#unrealengine', '#indiedev', 'combat', 'surgical', 'strike', 'vud2louegz', 'gain', 'grand', '@panther0698', 'loss', 'uma', '#stoker', 'ce', 'croods', '#thecroods', '#croodsnewage', '@thecroodsana', 'catch', 'age', 'releasing', 'thanksgiving', '@thecroodsfiim', 'grug', 'crood', 'croodsters', '#thecroods2', '#left4dead2', '#thelaststand', 'showing', 'update', 'map', '@gosoundtracks', '57th', 'prolific', 'composer', '#johnpowell', 'scoring', '#howtotrainyourdragon', '#solo', '@starwar', '#l4d2', '#logan', '#thewolverine', '#coh', 'rence', 'changer', '#coherence', 'ont', '@nebbiuco', 'journal', '20h', 'cha', 'danger', 'fondamentalisme', 'catholique', 'aimerait', 'reportage', 'versi', '#warmbodies', '#captainphillips', '@tomhanks', 'wednesday', 'sale', '@meninblack', '#21jumpstreet', '#castaway', '#lifeboats', 'miracles', 'adrift', '#theinternship', '#demon', '#horns', 'minha', 'saturday', 'wkh1t2rfau', '@mrkennykidd', 'crank', 'sumbitch', 'request', 'kenny', 'kidd', '#theloneranger', '#monstersuniversity', '#disney', '#monstersinc', '#whitehousedown', 'effects', '#sideeffects', 'forward', '#straykids', '@stray_kids', '[tiktok]', '#skzsideeffects', 'jsxubhgkdb', '#cl', '#locke', '#tomhardy', '#filmtwitter', 'tom', 'hardy', '#madmaxfuryroad', '#bane', '#inception', '#dunkirk', '#red2', '#beginagain', '@cdramal', 'fonte', 'estiver', 'certa', 'amanh', 'teremos', 'novidade', 'sobre', 'estou', 'ansiosa', 'ijs', '@maha_medd', 'prepared', 'csir', 'neeri', 'mumbai', 'cases', 'india', 'maharashtra', '#maharashtrafightscorona', 'makoto', 'keira', 'knightley', 'begin'])
In [33]:
X = mi_modelo[mi_modelo.wv.vocab]
matrix = pd.DataFrame(X)
matrix.index = mi_modelo.wv.vocab.keys()
matrix
C:\Users\villa\anaconda3\lib\site-packages\ipykernel_launcher.py:1: DeprecationWarning: Call to deprecated `__getitem__` (Method will be removed in 4.0.0, use self.wv.__getitem__() instead).
Out[33]:
| 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | ... | 90 | 91 | 92 | 93 | 94 | 95 | 96 | 97 | 98 | 99 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| going | -0.055651 | 0.071432 | 0.029310 | -0.034497 | 0.079875 | -0.174292 | -0.017052 | 0.131236 | -0.024584 | 0.022124 | ... | 0.007385 | -0.087558 | 0.168184 | -0.093564 | 0.046464 | 0.036513 | 0.068647 | -0.019770 | 0.013118 | -0.064592 |
| friends | -0.052632 | 0.067702 | 0.032422 | -0.037799 | 0.075110 | -0.168261 | -0.019775 | 0.127926 | -0.019085 | 0.020699 | ... | 0.005923 | -0.088170 | 0.168703 | -0.096785 | 0.042485 | 0.039790 | 0.066364 | -0.019543 | 0.021196 | -0.065220 |
| https | -0.077108 | 0.066397 | 0.038600 | -0.058942 | 0.094991 | -0.202156 | -0.016805 | 0.140857 | -0.035770 | 0.019121 | ... | -0.010523 | -0.109885 | 0.215048 | -0.125159 | 0.033530 | 0.057449 | 0.068438 | -0.011809 | 0.025576 | -0.066486 |
| co | -0.085285 | 0.075182 | 0.038038 | -0.053273 | 0.095134 | -0.194074 | -0.004835 | 0.143473 | -0.033643 | 0.025669 | ... | -0.004522 | -0.107078 | 0.210552 | -0.116677 | 0.028251 | 0.062933 | 0.076786 | -0.031840 | 0.021722 | -0.071530 |
| @youtube | -0.057348 | 0.064555 | 0.033929 | -0.037595 | 0.077567 | -0.170978 | -0.019677 | 0.128950 | -0.019155 | 0.022532 | ... | 0.007958 | -0.090864 | 0.170927 | -0.100306 | 0.038208 | 0.037403 | 0.061129 | -0.014652 | 0.017339 | -0.072036 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| #maharashtrafightscorona | -0.046239 | 0.052726 | 0.025841 | -0.028361 | 0.058334 | -0.136390 | -0.017997 | 0.095435 | -0.021823 | 0.023282 | ... | 0.001931 | -0.073746 | 0.134680 | -0.077404 | 0.035555 | 0.022533 | 0.053330 | -0.012128 | 0.010972 | -0.056316 |
| makoto | -0.033980 | 0.049039 | 0.019675 | -0.021468 | 0.051550 | -0.124817 | -0.010725 | 0.087206 | -0.020545 | 0.017908 | ... | 0.008816 | -0.067008 | 0.121294 | -0.064508 | 0.034095 | 0.023617 | 0.047110 | -0.008731 | 0.017469 | -0.047467 |
| keira | -0.042888 | 0.061493 | 0.021334 | -0.031538 | 0.059842 | -0.143878 | -0.016708 | 0.109372 | -0.017057 | 0.027338 | ... | 0.002835 | -0.076291 | 0.150229 | -0.086212 | 0.034067 | 0.030446 | 0.060657 | -0.014007 | 0.011991 | -0.054341 |
| knightley | -0.044607 | 0.050698 | 0.024785 | -0.022570 | 0.061570 | -0.141291 | -0.012155 | 0.101906 | -0.021182 | 0.015514 | ... | 0.005286 | -0.067979 | 0.132150 | -0.073708 | 0.036567 | 0.025689 | 0.049158 | -0.010641 | 0.012169 | -0.049859 |
| begin | -0.043838 | 0.060399 | 0.025822 | -0.029758 | 0.057626 | -0.140551 | -0.013583 | 0.101205 | -0.017143 | 0.019447 | ... | 0.001510 | -0.078162 | 0.141152 | -0.085160 | 0.030706 | 0.028080 | 0.056929 | -0.020563 | 0.010015 | -0.053086 |
883 rows × 100 columns
In [34]:
pca = PCA(n_components=2)
result = pca.fit_transform(matrix)
result = pd.DataFrame(result)
result.columns = ['X', 'Y']
result['Palabra'] = matrix.index.values
result
Out[34]:
| X | Y | Palabra | |
|---|---|---|---|
| 0 | 0.111621 | -0.006722 | going |
| 1 | 0.140899 | -0.003444 | friends |
| 2 | -0.032872 | 0.092856 | https |
| 3 | -0.037814 | 0.051788 | co |
| 4 | 0.104394 | -0.001040 | @youtube |
| ... | ... | ... | ... |
| 878 | 0.342251 | -0.005843 | #maharashtrafightscorona |
| 879 | 0.422889 | -0.006154 | makoto |
| 880 | 0.259490 | -0.007630 | keira |
| 881 | 0.323504 | -0.003752 | knightley |
| 882 | 0.285685 | -0.006350 | begin |
883 rows × 3 columns
In [35]:
trace = go.Scatter(x=result['X'].values,
y=result['Y'].values,
text=result['Palabra'].values,
mode='markers')
layout = go.Layout(title="PCA")
fig = go.Figure(data=trace, layout=layout)
iplot(fig)
Al ver la imagen encontramos representadas palabras del inglés, el español y el francés, lo que dificulta el análisis de contexto semántico. Seguramente esto es una muestra de que si bien existen una tendencia a expresar "el gusto o el disgusto" por una pelÃcula a partir de tradiciones lingüÃsticas similares, en esta visualización es dificil identificarlas. Segurmanete es una de las risegos que se toma al hacer el gráfico.
In [64]:
arreglo_final={}
matrix[0][0]
matrix[0].keys()
contador = 0
arreglo_temporal= list(range(100))
for filas_campos in matrix[0].keys():
arreglo_temporal2 =[]
for fila_arreglo in arreglo_temporal :
#arreglo_temporal2[fila_arreglo]=matrix[fila_arreglo][contador]
arreglo_temporal2.append(matrix[fila_arreglo][contador])
arreglo_final[filas_campos] = np.array(arreglo_temporal2)
contador+=1
arreglo_final
Out[64]:
{'going': array([-1.08756050e-01, -5.58795705e-02, 5.83606167e-03, 9.38337967e-02,
-7.44224116e-02, -7.22793862e-02, -1.99439317e-01, -5.82078658e-02,
7.62870386e-02, -1.22020349e-01, 1.95262298e-01, 1.65514290e-01,
7.40611479e-02, 7.37715885e-02, 1.46221712e-01, 2.95366496e-02,
8.45244713e-03, -2.46239919e-02, 6.64985254e-02, 1.06719084e-01,
1.65793151e-01, -1.38832197e-01, -1.84536248e-01, 5.69955185e-02,
1.86250091e-01, 6.61556497e-02, 7.17335194e-03, 2.02447157e-02,
1.40768932e-02, 4.20058072e-02, -4.33042757e-02, -7.15583116e-02,
1.28013790e-01, -8.26226175e-02, 8.43145326e-02, -4.77141440e-02,
-7.64917731e-02, -6.06438937e-03, 1.22013083e-03, -1.52850046e-03,
1.15376133e-04, -8.99487957e-02, 2.90588558e-01, -4.44773398e-02,
1.44679248e-01, 6.30894750e-02, -8.30356777e-02, 1.15109445e-03,
-6.65795282e-02, -1.15467273e-01, 8.96092579e-02, 1.49129331e-01,
1.18946679e-01, 4.78479788e-02, 2.80737374e-02, -1.14428988e-02,
1.08837364e-02, 4.54274863e-02, 2.43448280e-02, -7.81149417e-02,
5.99457063e-02, 6.99604675e-02, 1.14642657e-01, 1.56458601e-01,
-4.92329895e-02, 4.37526070e-02, -1.07675485e-01, 7.82858804e-02,
-4.83298078e-02, -1.07927276e-02, 2.46407643e-01, 1.41947687e-01,
-9.74324420e-02, 1.99820086e-01, 7.85803720e-02, 2.06243411e-01,
-2.03889096e-03, -2.07241967e-01, 1.25887021e-01, -5.47996759e-02,
9.76272896e-02, -6.27045557e-02, -1.40658304e-01, 2.53631435e-02,
-1.94490656e-01, -4.29565273e-02, -1.02447741e-01, 5.45343421e-02,
7.70561174e-02, -1.51488900e-01, -6.53185397e-02, 2.15487219e-02,
-4.51494418e-02, -8.55561867e-02, -5.90422302e-02, 7.98463635e-03,
2.10502058e-01, -1.05401836e-01, 1.76724970e-01, -1.32535055e-01],
dtype=float32),
'friends': array([-0.10351198, -0.05971197, 0.00495738, 0.09412779, -0.0769095 ,
-0.06394056, -0.1878549 , -0.06067096, 0.08237085, -0.12582633,
0.18555775, 0.16534981, 0.0784069 , 0.07134549, 0.13738534,
0.03602159, 0.00820662, -0.01294653, 0.06448583, 0.10274615,
0.16429284, -0.14055642, -0.17210975, 0.04973737, 0.17988922,
0.06659263, 0.00223329, 0.01889003, 0.01369192, 0.03501196,
-0.04459781, -0.07143185, 0.11782089, -0.08154159, 0.0838948 ,
-0.0493693 , -0.07430411, -0.00703317, 0.00194595, -0.00078822,
0.00559636, -0.09567408, 0.27727473, -0.04081935, 0.1365895 ,
0.0655341 , -0.08050643, 0.00528195, -0.07485001, -0.11258371,
0.08973274, 0.1361103 , 0.12159756, 0.05429649, 0.023293 ,
-0.00573492, 0.01295315, 0.03718423, 0.01842314, -0.08048391,
0.05690629, 0.06686544, 0.10613558, 0.15513003, -0.05386981,
0.04197413, -0.10035457, 0.07087502, -0.04010729, -0.01361117,
0.24237874, 0.1387341 , -0.09946275, 0.1866909 , 0.0833626 ,
0.20209768, -0.00431739, -0.1969403 , 0.11961959, -0.04909995,
0.09333813, -0.06697675, -0.14448264, 0.02794051, -0.18152611,
-0.04104157, -0.09997305, 0.05603046, 0.07137322, -0.14170204,
-0.0680203 , 0.02578962, -0.05113397, -0.09021363, -0.04917854,
0.00627553, 0.20383926, -0.09996665, 0.17370863, -0.12462243],
dtype=float32),
'https': array([-0.1320487 , -0.05288478, -0.0079078 , 0.11647928, -0.06815568,
-0.06070834, -0.2484034 , -0.06514204, 0.0858335 , -0.14417289,
0.21370135, 0.18628418, 0.0913736 , 0.09114821, 0.15882882,
0.04833033, -0.00113924, -0.03511988, 0.06592044, 0.12140599,
0.18326734, -0.16041563, -0.1933052 , 0.06624863, 0.23150024,
0.06417193, 0.00512845, 0.03211836, -0.00475895, 0.0346521 ,
-0.05925995, -0.06907243, 0.13550603, -0.11677988, 0.11235359,
-0.06536664, -0.09985799, -0.00472002, 0.02276058, 0.00113437,
-0.00872122, -0.10599237, 0.31793132, -0.0704696 , 0.15111765,
0.07766852, -0.11121693, -0.01337105, -0.07671313, -0.12947273,
0.10358118, 0.16396533, 0.1582061 , 0.05030115, 0.01974931,
-0.02860864, 0.01684884, 0.0501001 , 0.04112 , -0.09710214,
0.06020329, 0.07842404, 0.13870277, 0.16076146, -0.08807977,
0.07340565, -0.12905581, 0.06015028, -0.06455003, -0.01647915,
0.27619025, 0.17096364, -0.11401193, 0.2403996 , 0.09334541,
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Otro ejemplo para poner a prubea el modelo
In [65]:
palabras_cercanas(arreglo_final, "@starwar", n=5)
Out[65]:
['em', 'even', 'said', 'still', 'course']
Lo seguiremos mejorando
In [ ]: